AI Risk Management Framework for Automated File Identification
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Solution Overview
Problem
The increasing use of artificial intelligence (AI) in various applications poses challenges in identifying and managing associated risks, particularly in ensuring compliance with ethical and regulatory guidelines, as AI tools are often embedded without user awareness, necessitating automated tools for risk assessment and management.
Innovation Solution
An intelligent risk management framework that automates the identification of AI files, assigns risk scores based on attributes, and manages these risks by scanning file systems, analyzing dependencies, and performing automated tests to ensure compliance and ethical use of AI tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated scanning and analysis of all files in a repository is performed to identify AI-generated files, then the completeness and accuracy of risk identification is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the file repository into multiple directories and processes files in parallel using multiple threads. The scanning process is divided into: (1) obtaining a list of files, (2) scanning files in parallel using thread pools, (3) analyzing only suspected AI-generated files. This segmentation allows comprehensive scanning while distributing computational load efficiently across multiple processing units, reducing the energy cost per file while maintaining overall identification accuracy.
Solution Approach 2:
The patent performs preliminary scanning to identify suspected AI-generated files before conducting detailed analysis. The system first scans all files using lightweight detection methods (checking file metadata, creation timestamps, and basic patterns), then only applies resource-intensive analysis techniques to files flagged as suspicious. This preliminary filtering action significantly reduces the number of files requiring full analysis, thereby reducing overall computational resource consumption while maintaining high identification accuracy.
2Measurement precision
If comprehensive analysis of all file attributes and content is performed to identify AI-generated files, then the detection accuracy is improved, but the processing time increases
Solution Approach 1:
The detection process is segmented into multiple stages with increasing depth: (1) Quick scan checking basic file attributes and metadata, (2) Intermediate analysis examining file content patterns and structures, (3) Deep analysis performing comprehensive attribute checking only on suspected files. This multi-stage segmentation allows the system to quickly filter out non-AI files using minimal processing, then apply time-consuming detailed analysis only to a small subset of suspected files, thereby maintaining high detection accuracy while minimizing overall processing time.
Solution Approach 2:
The patent applies partial analysis to all files (checking basic attributes) and excessive/detailed analysis only to suspected files. Rather than performing comprehensive analysis on every file, the system does just enough initial scanning to identify candidates, then performs exhaustive analysis only on those candidates. This selective application of analysis depth maintains high detection accuracy for AI-generated files while avoiding wasteful processing of non-AI files.
3Measurement precision
If manual review and assessment of each identified AI file is conducted to evaluate risk levels, then the accuracy of risk assessment is improved, but the productivity and scalability of the risk management process decreases
Solution Approach 1:
The patent implements automated risk assessment where the system itself evaluates identified AI-generated files without requiring manual review. The automated assessment includes: (1) Analyzing file attributes and content patterns, (2) Comparing against known AI-generated file characteristics, (3) Assigning risk scores based on detection confidence and file properties, (4) Prioritizing files for further review based on assessed risk levels. This self-service automated assessment maintains high accuracy through algorithmic analysis while enabling scalable processing of large numbers of files, thereby resolving the contradiction between assessment accuracy and processing throughput.
4Reliability
If all identified AI files are subjected to detailed analysis and testing, then the reliability of risk management is improved, but the computational resources and processing efficiency deteriorate
Solution Approach 1:
The patent applies different levels of analysis quality to different files based on their suspected AI-generation probability. High-confidence suspected files receive comprehensive detailed analysis and testing, while low-confidence files receive lighter scrutiny. The system dynamically adjusts the depth and intensity of analysis based on local characteristics of each file (such as matching patterns, metadata anomalies, content structures), thereby maintaining high reliability for files that truly need scrutiny while improving overall processing efficiency by not applying maximum resources to every single file.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing the risks inherent in using machine learning and artificial intelligence (AI) tools. In one aspect, a system comprises accessing, by one or more computing devices, a repository of files, and analyzing, by the one or more computing devices, files stored in the repository to identify a subset of the files as candidates that contain or are potentially generated at least in part using one or more artificial intelligence (AI) processes. In another aspect, a system comprises identifying, based at least on one or more attributes indicative of artificial intelligence (AI) usage in a file, one or more automated tests to perform on the file, performing a user-selected automated test, storing results of the performed automated test to an inventory record, and using the results of the automated test to train a machine learning model.